b

c

error in

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error

error in contains an error

If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

cdf(x)

Cumulative probability that the continuous random variate has a value less than or equal to x.

error out

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error

error in contains an error

If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Algorithm Definition for the Continuous CDF of a Lognormal-Distributed Variate

The following equation defines the continuous CDF of a lognormal-distributed variate.

c
d
f
(
x
)
=
∫
−
∞
x
p
d
f
(
x
)
d
x

p
d
f
(
x
)
=
1
x
c
2
π
exp
⁡
(
−
[
log
⁡
(
x
b
)
]
2
2
c
2
)

where

x is the quantile of the continuous random variate

b is the scale parameter of the variate

c is the shape parameter of the variate

Where This Node Can Run:

Desktop OS: Windows

FPGA: This product does not support FPGA devices

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